In the resistance welding art it has long been recognized that it is desirable to monitor the condition of a weld during the weld process to evaluate the progress of weld formation and to use information about the satisfactory nugget formation to control the termination of weld current and to use information about weld quality or process variables to provide quality control statistics as well as indications of appropriate maintenance actions.
Many approaches to weld monitoring and control have been explored and success has been noted only in laboratory tests or in very carefully controlled production environments. Ordinary factory conditions introduce many variables which mitigate against successful detection of weld characteristics and adaptive control of the process. The following list suggests many of those variables:
1. Coating material PA0 2. Coating thickness variations PA0 3. Air pressure variations PA0 4. Air cylinder wear PA0 5. Rising tip force during a weld PA0 6. Gun stiffness variations PA0 7. Near-by metal (eddy current loss) PA0 8. Shunt paths (through adjacent welds) PA0 9. Grounding (shunt paths through fixturing) PA0 10. Water flow variations PA0 11. Weld material PA0 12. Number of stack-ups PA0 13. Sheet thickness PA0 14. Cap geometry and material PA0 15. Cable degradation (increased cable resistance) PA0 16. Weld through sealer PA0 17. Poor access PA0 18. Weld location near an edge PA0 19. Poor fit-up PA0 20. Cocked electrodes PA0 21. Line voltage fluctuations PA0 22. Changing height and number of stack-ups in a single weld pattern made by one gun PA0 23. Weld schedule and squeeze time PA0 24. Hold time, repeat time PA0 25. Surface contaminants PA0 26. Skid PA0 27. Part stiffness PA0 28. Electrode alignment PA0 29. Mechanical vibration during a weld PA0 30. Electrode wear PA0 31. Mechanical loading of the gun (typical in robot applications)
this can cause very rapid cap wear due to arcing PA1 this can also change the nugget formation time by a factor of two PA1 this can cause very rapid cap wear due to arcing PA1 this can cause uncontrolled nugget formation PA1 stiff guns do not allow electrode position to change during a weld. If the caps do move more energy may be required to form a weld at the new site. PA1 this can reduce the power supplied to the weld PA1 this can reduce the power supplied to the weld PA1 this can reduce the power supplied to the weld PA1 this can result in early electrode cap failure PA1 this can cause a wide variation in the amount of power that is required to form a weld PA1 cap geometry changes as more welds are made PA1 one cap style may not be used consistently PA1 this is difficult maintenance factor to predict PA1 causes off angle welding which requires more power PA1 causes electrical skidding during weld and affects the amount of energy required to make a weld PA1 fast repeat times cause gun heating which reduces the power supplied to welds PA1 contaminants such as paint, grease . . . can cause large changes in the time to make a weld PA1 this can come from many sources but the result is that more power is required to make a weld PA1 is a factor in the time required to align the caps to the sheet metal PA1 this can cause small or non-existent nuggets to be formed PA1 this can be caused by using improper shanks or flimsy guns PA1 causes erratic feedback control signals PA1 can cause poor nugget formation if it is not compensated for PA1 present day cap wear compensation is not feedback controlled so unexpected plant variations require manual adjustment PA1 this happens when an automatic weld gun is not aligned with the sheet metal. This causes a variance in the time for nugget formation because heat generation is a function of contact resistance which changes with the mechanical loading of the gun as well as many other variables.
For a weld monitor or control to be useful in most factory settings it should generally overcome these variables to help produce good welds or at least to recognize and report the kind of problems which require correction. It is recognized that extreme cases of some conditions will frustrate any attempt at real time recognition or resolution. Still, it is desired to overcome the weld condition variables in the majority of welds to monitor or control the weld process. It has been recognized that the weld expulsion event could be a good indicator of weld completion but heretofore an algorithm for recognizing the expulsion and its characteristics has not been available and in most spot weld applications the weld process has not been sufficiently stable to reliably analyze the weld parameters.